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- Title
基于时间关系的 Bi-LSTM+GCN 因果关系抽取.
- Authors
郑余祥; 左祥麟; 左万利; 梁世宁; 王 英
- Abstract
Aiming at the problem that traditional time relationships were only applied in the direction of machine learning, we proposed a relationship extraction method based on sequence labeling entity recognition. We first constructed Bi-LSTM model for feature extraction, and then input time relationship as a characteristic matrix for graph convolution.The experimental results show that the time relationship can improve the effect of causality extraction, and the Bi-LSTM +GCN model containing time relationship can effectively extract causal events, and the results of causality extraction of the Bi-LSTM+GCN model with time relationship are better than those of traditional methods.
- Subjects
MACHINE learning; VECTOR error-correction models
- Publication
Journal of Jilin University (Science Edition) / Jilin Daxue Xuebao (Lixue Ban), 2021, Vol 59, Issue 3, p643
- ISSN
1671-5489
- Publication type
Article
- DOI
10.13413/j.cnki.jdxblxb.2020152